In an R session

• run_point_verif()

run_point_verif(
  dttm       = seq_dttm(20251010, 20251020, "3h"),
  fcst_model = c("MEPS", "IFSENS"),
  param_defs = make_verif_param("S10m", verif_thresholds = seq(2.5, 10, 2.5)
  fcst_path  = "/data/harp/FCTABLE",
  obs_path   = "/data/harp/OBSTABLE",
  out_path   = "/data/harp/verification"
)

Helper functions

• make_verif_param()
  • param name
  • param names in files
  • scaling: make_scaling()
  • gross error check bounds
• make_verif_defaults()
  • groupings: make_verif_groups()
  • error check against obs sd

Batch running

• new_point_verif_project()
• DEMO!

Threshold weighted CRPS

• Computes CRPS emphasizing cases the fall within the threshold

• “Clamps” data outside the threshold of interest

Threshold weighted CRPS

Threshold weighted CRPS

UUI spread skill

\[\mathbb{E}[STDE_{p}] = 2\mathbb{E}[VARE_{P}]\]

UUI spread skill

\[\mathbb{E}[STDE_{P}] = 2\mathbb{E}[VARE_{P}]\]

• Based on the notion that the unbiased measure of spread is the expected difference between any two ensemble members \[ \small \begin{aligned} \mathbb{E}[(Y_{1}-Y_{2})^{2}] &=\mathbb{E}[Y_{1}-\mu+\mu-Y_{2}^{2}]\\ &=\mathbb{E}[(Y_{1}-\mu)^{2}]-2\mathbb{E}[Y_{1}-\mu]\mathbb{E}[Y_{2}-\mu]+\mathbb{E}[(Y_{2}-\mu)^{2}]\\ &=2\mathbb{E}[(Y-\mu)^{2}]-0\\ &=2Var(Y) \end{aligned} \] The 3rd equality comes from the fact \(Y_{1}\) and \(Y_{2}\) have the same distribution as \(Y\)

New plot method

• plot(data, col = )
• Uses ggplot so easily modifiable
• Demo!

Wind vectors

• geom_geowidvec(aes(u = , v = ), skip = )
• Skipping is important!
• Demo!

Neighbourhood probabilities

• ens_prob() for pixel probabilities
• nbhd_smooth() for neighbourhood probabilities
• Needs to be done member by member and aggregated
• Will (hopefully) be built into ens_prob() in the future
• Need to add ens_pfss() function
• Demo!

Cross sections

• Use transformation = "xsection" + xsection_opts()
• geom_xs() : model levels
• geom_xs_pressure() : model levels -> vertical pressure
  • needs:
    • surface pressure

Cross sections

• geom_xs_height() : model levels -> height ASL
  • needs:
    • surface pressure
    • temperature on model levels
    • specific humidity on model levels
    • representation of topographic height (usually surface geopotential)
• Demo!

• Just maintenance 😞